Introduction to Study Statistics Population Definition:-
The science of Statistics may be broadly studied under the following two headings:
The discussion to Descriptive Statistics which consists in describing some characters of the numerical data. The Inductive Statistics, also known as Statistical Inference, may be termed as the logic of drawing statistically valid conclusions about the totality of cases or items termed as population, in any statistical investigation on the basis of examining a part of the population termed as sample, and which is drawn from the population in scientific manner.
In any statistical investment the interest usually lies in studying the various characteristics relating to items or individuals belonging to a particular group. this group of individuals under study is known as the Population or Universe.
In statistics, population is the aggregate of objects, animate or inanimate, under study in any statistical investigation". In sampling theory, the population means the larger group from which the samples are drawn.
Finite population:- A population containing a finite number of objects or items is known as finite population.
Example:- The students in a college
Infinite population:- A population having an infinite number of objects is known as infinite population.
Example:- The population of stars in the sky.
Existent population:- A population consisting of concrete objects is known as existent population.
Example:- The population of the books in a library.
Hypothetical population:- If the population does not consist of concrete objects i.e., it consists of imaginary objects then it is called Hypothetical population.
Example:- The population of the throws of a die or a coin, thrown infinite number of times are hypothetical populations.
Sample:- A finite subset of the population is known as sample.
Size of the Sample:- The number of objects in a sample is called a size of the sample.
Sampling:- Sampling is a process of drawing samples from a given population.
The statistical constant of the population like mean ( `mu` ), variance (`sigma` 2 ), skewness ( `beta`1 ), kurtosis ( `beta` 2 ), moments ( `mu`r ), correlation coefficient ( `rho` ), etc.., are known as parameters.
The statistical constants of the sample like mean , variance ( s2 ), skewness ( b1 ), kurtosis ( b2 ), moments ( mr ), correlation coefficient ( r ), etc.., as statistics. Obviously, parameters are function of the population values while statistics are functions of the sample observations.